Giving credit where it's due, this project is almost worth doing just to be able to use that fabulously-well-fit initialism.
I'm pretty impressed. I miss the days of cool codenames and initialisms.
WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
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Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#12Giving credit where it's due, this project is almost worth doing just to be able to use that fabulously-well-fit initialism.
this was >50% of the motivation
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#13What would be legitimate civilian uses for this technology apart from [0]? After the 10k drone swarm the other day and the pager attacks all I can think of is slaughterbots, which is genuinely freaking me out. [0] - https://xkcd.com/2128/
Depending from how high it can reliably work from, collaborate with UK CCTV surveillance so that you can better track individuals with fewer cameras as long as you can collate them with cameras that confirm their position at various points in time.
Fly a handful of drones over the area of a fleeing suspect and be able to track their whereabouts and look for suspicious behaviors (eg. someone running and making constant turns in a city or doubling back often, cutting through alleys).
Hell fly a few drones of the city to monitor foot traffic of the population and determine possible points of interest for new developments. Where are people walking to? How do they tend to get there? Can we optimize traffic for them - or more realistically - around them?
Could be used for other forms of crowd analysis too such as how to best disperse a riot and separate a crowd.
Sorry I guess I'm about as pessimistic as you are about it. Use in S&R like throwup238 suggested seems like a good non-militaristic fit for it.
Oh and also this which was posted on HN not too long ago: https://dropofahat.zone/
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#14So it gives us, for all the objects in view, a unique id, location, location history, various alerts. What else? Any thing-description? If an object leaves the view and re-enters, does it get the same id?
It's just yolo-esque classid, bbox coords, confidence. You'll have to implement some sort of tracking algorithm to get your other traits.
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#15Hey, thanks for posting. New release is coming tomorrow on HF BTW. AMA
Cool project, any specific reason you went with YOLOv7?
I know you aren't going to release the dataset but I'd be interesting in any info you are willing to share on augmentations you used and how you generated the synthetic imagery, and what sort of lift you got out of it.
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#16> 'arm/mil' --> this class detects certain types of armored vehicles (very unreliable for now, don't use it yet) Living near a bunch of the military bases, this is what I really need. My suburban defense system keeps mistaking USPS trucks for APCs. I haven’t received any mail for months. Sidenote: what are the export restriction?
that class never really worked and has been removed from the new version of WALDO FYI, it’s not a military thing and shouldn’t be used as such
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#17Hey, thanks for posting. New release is coming tomorrow on HF BTW. AMA
Cool project, any specific reason you went with YOLOv7? I know you aren't going to release the dataset but I'd be interesting in any info you are willing to share on augmentations you used and how you generated the synthetic imagery, and what sort of lift you got out of it.
Some of the design choices of YOLOv7 make more sense to me in the choices of default augmentations and the structures of the very large versions of the networks. I find I can push it to marginally better recall. It’s slower than Ultralytics’ V8 but if you want to do stuff like offline processing of satellite imagery for instance or get 1fps on occupancy of a parking lot that kind of performance really doesn’t matter.
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#18I worked for a place where we needed to know with precision where in space a large object was relative to a large area we had full control over. I wonder if this could be used in reverse by say dropping QR codes on the ground, using the algorithm to track relative positions and doing the reverse operation from there
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#19> 'arm/mil' --> this class detects certain types of armored vehicles (very unreliable for now, don't use it yet) Living near a bunch of the military bases, this is what I really need. My suburban defense system keeps mistaking USPS trucks for APCs. I haven’t received any mail for months. Sidenote: what are the export restriction?
Ai is going to super charge off grid antigov nuts libertarians?
Re: WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
#20I wonder if these achievements are related to war in Ukraine. Do scientists suddenly receive more funding or something? Or it just happens? Is there a non public version with very reliable arm/mil? Is there a version which can reliably distinguish T-80 with and without Z?
A big part is that training image detection is incredibly easy today. YOLO is a great network with reasonably intuitive tooling. Anyone with a set of images can start labeling them, copy-paste a couple lines into a jupyter notebook and make a decent YOLO finetune.
The difficulty is in the training data, both acquiring it and labeling it. Hence why the readme of WALDO alludes so much to their semi-synthetic data. That's also why this commercial project is happy to give out the models, but doesn't publish their data pipeline.
If you have about 100 satellite images each of T-80s with and without Zs, and a couple other satellite images of other tanks and of landscapes without any tanks you can train a T-80 detecting model in a couple hours. And then spend a couple days in a rabbit hole where you figure out that because in your training set only images with tanks had smoke clouds the model now thinks that smoke clouds are linked to tanks, and you end up making larger and larger data sets with tanks and non-tanks from all angles.